- Tytuł:
- New event based H∞ state estimation for discrete-time recurrent delayed semi-markov jump neural networks via a novel summation inequality
- Autorzy:
-
Cao, Yang
Maheswari, K.
Dharan, S.
Sivaranjani, K. - Powiązania:
- https://bibliotekanauki.pl/articles/2147136.pdf
- Data publikacji:
- 2022
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
discrete-time neural networks
mixed time delays
asymptotic stability
eventtriggered control - Opis:
- This paper investigates the event-based state estimation for discrete-time recurrent delayed semi-Markovian neural networks. An event-triggering protocol is introduced to find measurement output with a specific triggering condition so as to lower the burden of the data communication. A novel summation inequality is established for the existence of asymptotic stability of the estimation error system. The problem addressed here is to construct an H∞ state estimation that guarantees the asymptotic stability with the novel summation inequality, characterized by event-triggered transmission. By the Lyapunov functional technique, the explicit expressions for the gain are established. Finally, two examples are exploited numerically to illustrate the usefulness of the new methodology.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 3; 207--227
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł